Optimal Output Gain Algorithm For Feed-forward Network Training

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Title: Optimal Output Gain Algorithm For Feed-forward Network Training
Author: Aswathappa, Babu Hemanth Kumar
Abstract: A batch training algorithm for feed -forward networks is proposed which uses Newton's method to estimate a vector of optimal scaling factors for output errors in the network . Using this vector , backpropagation is used to modify weights feeding into the hidden units . Linear equations are then solved for the network's output weights . Elements of the new method's Gauss -Newton Hessian matrix are shown to be weighted sums of elements from the total network's Hessian . The effect of output transformation on training a feed -forward network is reviewed and explained , using the concept of equivalent networks . In several examples , the new method performs better than backpropagation and conjugate gradient , with similar numbers of required multiplies . The method performs about as well as Levenberg -Marquardt , with several orders of magnitude fewer multiplies due to the small size of its Hessian .
URI: http : / /hdl .handle .net /10106 /5510
Date: 2011-03-03

Citation

Optimal Output Gain Algorithm For Feed-forward Network Training. Available electronically from http : / /hdl .handle .net /10106 /5510 .

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